{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial of TaPiTas in pysda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pysda\n",
    "import os\n",
    "import pandas as pd\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the build-in functions to read data (highly recommend)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = r\"/home/benny/Workspaces/pySDA/change1022/test_data\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1: Load .csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"DengueKS2014.csv\"\n",
    "path = os.path.join(folder, filename)\n",
    "crs=\"+init=epsg:4326\"\n",
    "\n",
    "pysda_data = pysda.data.readCSV(path, xtitle=\"X\", ytitle=\"Y\", ttitle=\"OnsetDay\", crs=crs, tunit=\"day\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2: Load DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"DengueKS2014.csv\"\n",
    "path = os.path.join(folder, filename)\n",
    "crs=\"+init=epsg:4326\"\n",
    "df = pd.read_csv(path, encoding=\"utf-8\")\n",
    "\n",
    "pysda_data = pysda.data.readDF(df, xtitle=\"X\", ytitle=\"Y\", ttitle=\"OnsetDay\", crs=crs, tunit=\"day\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 3: Load .shp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"DengueKS2014.shp\"\n",
    "path = os.path.join(folder, filename)\n",
    "\n",
    "pysda_data = pysda.data.readSHP(path, ttitle=\"OnsetDay\", tunit=\"day\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 4: Load geoDataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"DengueKS2014.shp\"\n",
    "path = os.path.join(folder, filename)\n",
    "gdf = gpd.read_file(path, encoding=\"utf-8\")\n",
    "\n",
    "pysda_data = pysda.data.readGDF(gdf, ttitle=\"OnsetDay\", tunit=\"day\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Arguments explanation:\n",
    "\n",
    "1. **xtitle** is the name of the column which records the x coordinate of each point.\n",
    "2. **ytitle** is the name of the column which records the y coordinate of each point.  \n",
    "Note: The values of x and y must be **projected** coordinates rather than longitude and latitude.\n",
    "3. **crs** is the Coordinate Reference System of the x and y.\n",
    "\n",
    "4. **ttitle** is the name of the column which records the time of each point either in integer format or in date format. If its values are in integer format, pysda will directly use them as the time stamps; otherwise, pysda will firstly transform them into integer format (through *tunit* argument).\n",
    "\n",
    "5. **tunit** is the temporal resolution for analysis, and the first time stamp is the first date in the input data. There are several choices:\n",
    "    - int: assign that the ttitle column in the input data is integer format.\n",
    "    - hour: 1 hour  \n",
    "    - day: 1 days\n",
    "    - week: 7 days\n",
    "    - month: 30 days\n",
    "    - year: 365 days\n",
    "    - tunit also can be any string accepted by pandas' *date_range* function. For more details, please refer to https://pandas.pydata.org/pandas-docs/stable/timeseries.html#timeseries-offset-aliases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "***\n",
    "### Initialize an instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tpt = pysda.Tapitas(pysda_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set the parameters\n",
    "\n",
    "parameters:\n",
    "- s_radius: spatial search radius\n",
    "- T1: the minimum value of the temporal search window.  \n",
    "  If the $\\Delta T \\leq T1$ meaning that the relationship between the two events are neighboring pair \n",
    "- T2: the maximum value of the temporal search window.  \n",
    "  If the $T1 < \\Delta T \\leq T2$ meaning that the relationshp between the two events are shifting link\n",
    "\n",
    "For more details about the meanging of each parameter, please refer to the following documents:\n",
    "- journal article: https://www.nature.com/articles/s41598-017-12852-z\n",
    "- tapitas package: https://bitbucket.org/wcchin/tapitas/src"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_radius = 300\n",
    "T1 = 6\n",
    "T2 = 23\n",
    "\n",
    "tpt.setParams(T1=T1, T2=T2, SR=s_radius, resample=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start running the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "construction of shifting graph - start\n",
      "construction of shifting graph - stop\n",
      "making links - start\n",
      "find links - start\n",
      "find links - stop\n",
      "calculate passing possibility - start\n",
      "calculate passing possibility - stop\n",
      "calculate propensity - start\n",
      "calculate propensity - stop\n",
      "making links - stop\n",
      "bootstraping - start\n",
      "critical value is:  0.12924023944493063\n",
      "bootstraping - stop\n",
      "detection of subclusters - start\n",
      "number of subcluster found: 112\n",
      "calculation done\n",
      "prepare results table\n",
      "prepare result done\n"
     ]
    }
   ],
   "source": [
    "tpt.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### show the summary of the results\n",
    "an overview of the results\n",
    "- nodes: number of nodes\n",
    "- npair: number of neighboring pair\n",
    "- slink: number of shifting link\n",
    "- sub-cluster: number of detected sub-cluster\n",
    "- final_cpair: the cluster pair (neighboring pair with high probability to have the common origin)\n",
    "- final_slink: the remaining shifting links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sub-cluster</th>\n",
       "      <td>112.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>critical_value</th>\n",
       "      <td>0.12924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>final_cpair</th>\n",
       "      <td>1865.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>final_slink</th>\n",
       "      <td>1285.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nodes</th>\n",
       "      <td>1320.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npair</th>\n",
       "      <td>7257.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>progressno</th>\n",
       "      <td>43.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slink</th>\n",
       "      <td>12948.00000</td>\n",
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      ],
      "text/plain": [
       "                  attribute\n",
       "sub-cluster       112.00000\n",
       "critical_value      0.12924\n",
       "final_cpair      1865.00000\n",
       "final_slink      1285.00000\n",
       "nodes            1320.00000\n",
       "npair            7257.00000\n",
       "progressno         43.00000\n",
       "slink           12948.00000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpt.summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get all results\n",
    "\n",
    "the getALL() function will return a dictionary, using these keys:\n",
    "- nodes: a point geodataframe with all nodes inside, and which sub-cluster it belong to. \n",
    "- npairs: a line geodataframe recording all neighboring pairs (based on T1 and s_radius setting).\n",
    "- slinks: a line geodataframe recording all shifting links indicating the transmission relationship.\n",
    "- subclsuters: the detected sub-clusters, using the standard ellipse of the points to represent the location and direction. \n",
    "- prog_links: the connections between sub-clusters, which is consisted of one or more shifting links. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key is nodes\n",
      "Key is slinks\n",
      "Key is npairs\n",
      "Key is subclusters\n",
      "Key is prog_links\n"
     ]
    }
   ],
   "source": [
    "result = tpt.getAll()\n",
    "for key, value in result.items():\n",
    "    print(\"Key is\", key)\n",
    "    #print(value.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### nodes\n",
    "- node_id is same as the order of the input data\n",
    "- xx: the x-coordinate of the point\n",
    "- yy: the y-coordinate of the point\n",
    "- time: the time column\n",
    "- clid: the sub-cluster id the node belong to\n",
    "- chid: the progression chain id the node belong to\n",
    "- in_size: the number of shifting link that pointed to this node\n",
    "- out_size: the number of shifting link that pointed outward from this node\n",
    "- neig_size: the number of neighboring pair that connected with this node\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2014/05/23-00:00:00</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>18</td>\n",
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       "   node_id             xx            yy                 time  clid  chid  \\\n",
       "0        0  179695.200767  2.497270e+06  2014/05/13-00:00:00    -1   NaN   \n",
       "1        1  179695.200767  2.497270e+06  2014/05/13-00:00:00    -1   NaN   \n",
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       "3        3  179695.200767  2.497270e+06  2014/05/22-00:00:00     0  -1.0   \n",
       "4        4  181420.648698  2.497000e+06  2014/05/22-00:00:00    -1   NaN   \n",
       "5        5  180060.529509  2.497776e+06  2014/05/23-00:00:00    -1   NaN   \n",
       "6        6  180317.340190  2.497715e+06  2014/05/24-00:00:00    -1   NaN   \n",
       "7        7  179695.200767  2.497270e+06  2014/05/25-00:00:00     0  -1.0   \n",
       "8        8  179695.200767  2.497270e+06  2014/05/26-00:00:00     0  -1.0   \n",
       "9        9  177549.124107  2.497523e+06  2014/05/28-00:00:00    -1   NaN   \n",
       "\n",
       "   in_size  out_size  neig_size                                     geometry  \n",
       "0        0         3          1   POINT (179695.200766882 2497269.858528782)  \n",
       "1        0         3          1   POINT (179695.200766882 2497269.858528782)  \n",
       "2        0        16          2  POINT (180211.6435065586 2497764.701022268)  \n",
       "3        2         0          2   POINT (179695.200766882 2497269.858528782)  \n",
       "4        0         6          0  POINT (181420.6486981525 2496999.610708646)  \n",
       "5        0        18          2  POINT (180060.5295093319 2497776.462549706)  \n",
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       "7        2         0          2   POINT (179695.200766882 2497269.858528782)  \n",
       "8        2         0          2   POINT (179695.200766882 2497269.858528782)  \n",
       "9        0         0          0  POINT (177549.1241071712 2497523.457340129)  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['nodes'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### neighboring pairs\n",
    "- n1_id / n2_id: the node_id of the two endpoints of the neighboring pair\n",
    "- clid: the sub-cluster id the neighboring pair is in\n",
    "- chid: the progression chain the neighboring pair is in\n",
    "- max_cop: the max common origin prob.(COP) within the list of COP of all common origin between the two node\n",
    "- n1x/n1y/n2x/n2y: the coordinates of the two nodes\n",
    "- n1t/n2t: the time of the two node\n",
    "- distance: the geographical distance between the two nodes\n",
    "- timelag: the time difference between the two node\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>n1_id</th>\n",
       "      <th>n2_id</th>\n",
       "      <th>clid</th>\n",
       "      <th>chid</th>\n",
       "      <th>max_cop</th>\n",
       "      <th>n1x</th>\n",
       "      <th>n1y</th>\n",
       "      <th>n2x</th>\n",
       "      <th>n2y</th>\n",
       "      <th>n1t</th>\n",
       "      <th>n2t</th>\n",
       "      <th>distance</th>\n",
       "      <th>timelag</th>\n",
       "      <th>geometry</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>2014/05/22-00:00:00</td>\n",
       "      <td>2014/05/25-00:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>LINESTRING (179695.200766882 2497269.858528782...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>2014/05/22-00:00:00</td>\n",
       "      <td>2014/05/26-00:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>LINESTRING (179695.200766882 2497269.858528782...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>179695.200767</td>\n",
       "      <td>2.497270e+06</td>\n",
       "      <td>2014/05/25-00:00:00</td>\n",
       "      <td>2014/05/26-00:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>LINESTRING (179695.200766882 2497269.858528782...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.399496</td>\n",
       "      <td>180118.534518</td>\n",
       "      <td>2.497868e+06</td>\n",
       "      <td>180060.529509</td>\n",
       "      <td>2.497776e+06</td>\n",
       "      <td>2014/05/31-00:00:00</td>\n",
       "      <td>2014/06/01-00:00:00</td>\n",
       "      <td>108.459745</td>\n",
       "      <td>1</td>\n",
       "      <td>LINESTRING (180118.5345183359 2497868.10825491...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.179460</td>\n",
       "      <td>180118.534518</td>\n",
       "      <td>2.497868e+06</td>\n",
       "      <td>180000.459246</td>\n",
       "      <td>2.497909e+06</td>\n",
       "      <td>2014/05/31-00:00:00</td>\n",
       "      <td>2014/06/05-00:00:00</td>\n",
       "      <td>124.796460</td>\n",
       "      <td>5</td>\n",
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      "text/plain": [
       "   n1_id  n2_id  clid  chid   max_cop            n1x           n1y  \\\n",
       "0      3      7     0    -1  0.250000  179695.200767  2.497270e+06   \n",
       "1      3      8     0    -1  0.250000  179695.200767  2.497270e+06   \n",
       "2      7      8     0    -1  0.250000  179695.200767  2.497270e+06   \n",
       "3     10     11     1     0  0.399496  180118.534518  2.497868e+06   \n",
       "4     10     13     1     0  0.179460  180118.534518  2.497868e+06   \n",
       "\n",
       "             n2x           n2y                  n1t                  n2t  \\\n",
       "0  179695.200767  2.497270e+06  2014/05/22-00:00:00  2014/05/25-00:00:00   \n",
       "1  179695.200767  2.497270e+06  2014/05/22-00:00:00  2014/05/26-00:00:00   \n",
       "2  179695.200767  2.497270e+06  2014/05/25-00:00:00  2014/05/26-00:00:00   \n",
       "3  180060.529509  2.497776e+06  2014/05/31-00:00:00  2014/06/01-00:00:00   \n",
       "4  180000.459246  2.497909e+06  2014/05/31-00:00:00  2014/06/05-00:00:00   \n",
       "\n",
       "     distance  timelag                                           geometry  \n",
       "0    0.000000        3  LINESTRING (179695.200766882 2497269.858528782...  \n",
       "1    0.000000        4  LINESTRING (179695.200766882 2497269.858528782...  \n",
       "2    0.000000        1  LINESTRING (179695.200766882 2497269.858528782...  \n",
       "3  108.459745        1  LINESTRING (180118.5345183359 2497868.10825491...  \n",
       "4  124.796460        5  LINESTRING (180118.5345183359 2497868.10825491...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['npairs'].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### shifting links\n",
    "- ooid: the node id of the origin\n",
    "- ddid: the node id of the destination\n",
    "- clid: the sub-clsuter id of the destination point\n",
    "- chid: the progression chain id of the destination point\n",
    "- srisk: the spatial risk\n",
    "- trisk: the temporal risk\n",
    "- crisk: the combine risk\n",
    "- opossi: the origin possibility\n",
    "- oxcor / oycor: the coordinate of the origin point\n",
    "- otime: the time of the origin point\n",
    "- dxcor / dycor: the coordinate of the destination point\n",
    "- dtime: the time of the destination point\n",
    "- distance: the geographical distance between the two points\n",
    "- timelag: the time differences between the two points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "   ooid  ddid  clid  chid     srisk     trisk     crisk    opossi  \\\n",
       "0    11    47     1   0.0  0.244791  0.876032  0.214444  0.412321   \n",
       "1   820  1116    65  -1.0  1.000000  0.168737  0.168737  0.295566   \n",
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       "4   575  1092    46  -1.0  0.254201  0.692362  0.175999  0.175145   \n",
       "\n",
       "           oxcor         oycor                otime          dxcor  \\\n",
       "0  180060.529509  2.497776e+06  2014/06/01-00:00:00  180211.643507   \n",
       "1  182245.094313  2.501114e+06  2014/08/16-00:00:00  182245.094313   \n",
       "2  181244.338495  2.502401e+06  2014/07/18-00:00:00  181239.417198   \n",
       "3  181255.234077  2.502309e+06  2014/07/25-00:00:00  181380.687601   \n",
       "4  182652.347040  2.501608e+06  2014/08/08-00:00:00  182685.882678   \n",
       "\n",
       "          dycor                dtime    distance  timelag  \\\n",
       "0  2.497765e+06  2014/06/17-00:00:00  151.571019       16   \n",
       "1  2.501114e+06  2014/08/25-00:00:00    0.000000        9   \n",
       "2  2.502450e+06  2014/08/01-00:00:00   48.993402       14   \n",
       "3  2.502316e+06  2014/08/10-00:00:00  125.659322       16   \n",
       "4  2.501753e+06  2014/08/25-00:00:00  148.744965       17   \n",
       "\n",
       "                                            geometry  \n",
       "0  LINESTRING (180060.5295093319 2497776.46254970...  \n",
       "1  LINESTRING (182245.0943129217 2501114.16939420...  \n",
       "2  LINESTRING (181244.338494828 2502400.940853412...  \n",
       "3  LINESTRING (181255.2340772641 2502308.98168802...  \n",
       "4  LINESTRING (182652.3470396374 2501608.46578799...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['slinks'].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### sub-clusters\n",
    "- clid: sub-cluster id\n",
    "- chid: progression chain id\n",
    "- xx / yy: the mean center coordinate\n",
    "- cls_size: number of point in this sub-cluster\n",
    "- time_mdian: median time of the points in this sub-cluster\n",
    "- time_start: the first point \n",
    "- time_stop: the last point\n",
    "- behaviors: the sub-cluster behaviors\n",
    "- in_count: the number of incoming sub-cluster (progression link)\n",
    "- out_count: the number of outgoing sub-cluster (progression link)\n",
    "- cls_area: the area of the standard ellipse of the sub-cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "      <td>44</td>\n",
       "      <td>-1</td>\n",
       "      <td>182517.056325</td>\n",
       "      <td>2.503802e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>2014/08/07-00:00:00</td>\n",
       "      <td>2014/08/05-00:00:00</td>\n",
       "      <td>2014/08/10-00:00:00</td>\n",
       "      <td>['appearing', 'disappearing']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((182517.0563254996 2503802.168012453,...</td>\n",
       "      <td>4.796163e-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    clid  chid             xx            yy  cls_size           time_mdian  \\\n",
       "0      0    -1  179695.200767  2.497270e+06         3  2014/05/25-00:00:00   \n",
       "37    37    -1  184607.625146  2.496512e+06         4  2014/08/03-00:00:00   \n",
       "40    40    -1  178323.287580  2.508678e+06         2  2014/08/04-00:00:00   \n",
       "43    43    -1  176905.909885  2.504535e+06         2  2014/08/06-00:00:00   \n",
       "44    44    -1  182517.056325  2.503802e+06         2  2014/08/07-00:00:00   \n",
       "\n",
       "             time_start            time_stop                      behaviors  \\\n",
       "0   2014/05/22-00:00:00  2014/05/26-00:00:00  ['appearing', 'disappearing']   \n",
       "37  2014/08/02-00:00:00  2014/08/06-00:00:00  ['appearing', 'disappearing']   \n",
       "40  2014/08/03-00:00:00  2014/08/05-00:00:00  ['appearing', 'disappearing']   \n",
       "43  2014/08/05-00:00:00  2014/08/08-00:00:00  ['appearing', 'disappearing']   \n",
       "44  2014/08/05-00:00:00  2014/08/10-00:00:00  ['appearing', 'disappearing']   \n",
       "\n",
       "    in_count  out_count                                           geometry  \\\n",
       "0          0          0  POLYGON ((179696.0112203408 2497271.120735259,...   \n",
       "37         0          0  POLYGON ((184607.6251457435 2496511.762124397,...   \n",
       "40         0          0  POLYGON ((178324.0980335926 2508678.842451562,...   \n",
       "43         0          0  POLYGON ((176906.7203385521 2504536.254855041,...   \n",
       "44         0          0  POLYGON ((182517.0563254996 2503802.168012453,...   \n",
       "\n",
       "        cls_area  \n",
       "0   7.057234e+00  \n",
       "37  1.421085e-14  \n",
       "40  7.057234e+00  \n",
       "43  7.057234e+00  \n",
       "44  4.796163e-14  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['subclusters'].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### progression links\n",
    "- id0: the sub-cluster id of the origin\n",
    "- id1: the sub-cluster id of the destination\n",
    "- clid: the sub-cluster id of the destination\n",
    "- chid: the chain id\n",
    "- size0: the number of node in the origin sub-cluster\n",
    "- size1: the number of node in the destination sub-cluster\n",
    "- x0/x1/y0/y1: the **mean center** coordinates of the origin(end with 0) and destination(end with 1) sub-clusters\n",
    "- t0: the time of the first point in the origin sub-cluster\n",
    "- t1: the time of the last point in destination sub-cluster\n",
    "- op: origin possibility\n",
    "- no_SL: the number of shifting links that connect from a node in origin sub-cluser to a node in the destination sub-cluster\n",
    "- distance: the geographical distance between the **mean center** of the two sub-clusters\n",
    "- timelag: the $t_1-t_0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id0</th>\n",
       "      <th>id1</th>\n",
       "      <th>clid</th>\n",
       "      <th>chid</th>\n",
       "      <th>size0</th>\n",
       "      <th>size1</th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>y0</th>\n",
       "      <th>y1</th>\n",
       "      <th>t0</th>\n",
       "      <th>t1</th>\n",
       "      <th>op</th>\n",
       "      <th>no_SL</th>\n",
       "      <th>distance</th>\n",
       "      <th>timelag</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>4</td>\n",
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       "      <td>2.502283e+06</td>\n",
       "      <td>2.502447e+06</td>\n",
       "      <td>2014/08/05-00:00:00</td>\n",
       "      <td>2014/08/29-00:00:00</td>\n",
       "      <td>0.465578</td>\n",
       "      <td>7</td>\n",
       "      <td>286.805085</td>\n",
       "      <td>24</td>\n",
       "      <td>LINESTRING (181462.1460887853 2502283.05990330...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>64</td>\n",
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       "      <td>4</td>\n",
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       "      <td>181207.254755</td>\n",
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       "      <td>2.502474e+06</td>\n",
       "      <td>2.502585e+06</td>\n",
       "      <td>2014/07/14-00:00:00</td>\n",
       "      <td>2014/08/15-00:00:00</td>\n",
       "      <td>0.309481</td>\n",
       "      <td>3</td>\n",
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       "      <th>2</th>\n",
       "      <td>55</td>\n",
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       "      <td>2.499393e+06</td>\n",
       "      <td>2.499283e+06</td>\n",
       "      <td>2014/08/08-00:00:00</td>\n",
       "      <td>2014/08/31-00:00:00</td>\n",
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       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>181265.985671</td>\n",
       "      <td>181106.644609</td>\n",
       "      <td>2.501089e+06</td>\n",
       "      <td>2.500861e+06</td>\n",
       "      <td>2014/08/04-00:00:00</td>\n",
       "      <td>2014/08/21-00:00:00</td>\n",
       "      <td>0.795686</td>\n",
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       "      <td>2.499531e+06</td>\n",
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       "      <td>2014/07/19-00:00:00</td>\n",
       "      <td>2014/08/03-00:00:00</td>\n",
       "      <td>0.477339</td>\n",
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      ],
      "text/plain": [
       "   id0  id1  clid  chid  size0  size1             x0             x1  \\\n",
       "0   42   92    92     4     21      5  181462.146089  181697.496889   \n",
       "1   12   64    64     4     95      3  181207.254755  181383.949445   \n",
       "2   55   80    80    16      9      9  181071.921987  181186.817842   \n",
       "3   41   68    68    11     23      5  181265.985671  181106.644609   \n",
       "4   18   36    36     1      2      2  180628.914532  180664.139356   \n",
       "\n",
       "             y0            y1                   t0                   t1  \\\n",
       "0  2.502283e+06  2.502447e+06  2014/08/05-00:00:00  2014/08/29-00:00:00   \n",
       "1  2.502474e+06  2.502585e+06  2014/07/14-00:00:00  2014/08/15-00:00:00   \n",
       "2  2.499393e+06  2.499283e+06  2014/08/08-00:00:00  2014/08/31-00:00:00   \n",
       "3  2.501089e+06  2.500861e+06  2014/08/04-00:00:00  2014/08/21-00:00:00   \n",
       "4  2.499531e+06  2.499589e+06  2014/07/19-00:00:00  2014/08/03-00:00:00   \n",
       "\n",
       "         op  no_SL    distance  timelag  \\\n",
       "0  0.465578      7  286.805085       24   \n",
       "1  0.309481      3  208.866867       32   \n",
       "2  0.448296     13  159.332120       23   \n",
       "3  0.795686      7  278.489359       17   \n",
       "4  0.477339      2   68.312662       15   \n",
       "\n",
       "                                            geometry  \n",
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       "1  LINESTRING (181207.2547546971 2502473.86762932...  \n",
       "2  LINESTRING (181071.9219865342 2499393.41265436...  \n",
       "3  LINESTRING (181265.9856705102 2501088.96968905...  \n",
       "4  LINESTRING (180628.9145322346 2499530.69172823...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['prog_links'].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Directly save the results to the perfered direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "tpt.saveAll(dirpath='temp', prefix='temp2_', to_csv=True, to_shp=True, zip_it=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the figure "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "use sns hls color list with 5 groups\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tpt.saveFigure(dirpath='temp', prefix='temp2_', vno=16, dev_scale=1.5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
